code: def test_1_binary(self):
      df = pd.read_csv("tests/data/ionosphere.csv")[:100]
      target = "target"
  
      pdef = ProblemDefinition.from_dict(
          {"target": target, "time_aim": 20, "unbias_target": False}
      )
      jai = json_ai_from_problem(df, pdef)
  
      jai.model["args"]["submodels"] = [
          {
              "module": "RandomForest",
              "args": {
                  "stop_after": "$problem_definition.seconds_per_mixer",
                  "fit_on_dev": True,
              },
          }
      ]
      code = code_from_json_ai(jai)
      predictor = predictor_from_code(code)
  
      predictor.learn(df)
      predictions = predictor.predict(df)
  
      acc = balanced_accuracy_score(
          df[target], predictions["prediction"]
      )
      self.assertTrue(acc > 0.5)
      self.assertTrue(
          all([0 <= p <= 1 for p in predictions["confidence"]])
      )
  
file_path: lightwood/tests/unit_tests/mixer/test_random_forest.py
imports: import unittest
  import numpy
  import pandas
  from sklearn.metrics import balanced_accuracy_score
  from lightwood.api.types import ProblemDefinition
  from lightwood.api.high_level import json_ai_from_problem, predictor_from_json_ai, JsonAI, code_from_json_ai, predictor_from_code
label_desc: Use image node_id 3 for calling the TestBasic obj's underlying member method code with example usage: obj.test_1_binary() without return types